More Accurate and Efficient Analysis for Automatic Speech Recognition

更准确、更高效的自动语音识别分析

• 批准号: 914-2013
• 负责人: OShaughnessy, Douglas
• 金额: $ 2.11万
• 依托单位: Institut national de la recherche scientifique
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637720
• 关键词: More; Accurate; Efficient; Analysis; Automatic

Efficient communicating with machines via voice will facilitate interactions that so far have been hindered by awkward interfaces such as keyboards and telephone keypads. People nowadays have increasing need to interact with computers, yet this is still done mostly by typing. Even attempts to seek information by telephone often require many cycles of listening to long messages and then pushing a button, because our capability to do reliable automatic speech recognition (ASR) is quite limited. For some time now, refinements to basic ASR methods established years ago have improved performance, without radical changes to the basic approaches. The recent vast increase in computational power and memory size has led researchers to attack increasingly difficult tasks such as continuously-spoken, very-large-vocabulary, speaker-independent, noisy speech over the telephone. For some limited tasks, e.g., recognizing credit card numbers, or words drawn from medium-sized vocabularies and spoken with frequent pauses, recognition accuracy rises above 99%, and hence practical commercial products are available. However, progress has been slow for the more difficult tasks of recognizing conversational speech or noisy speech. Furthermore, recognition error rates remain high when generalized speaker-independent models are used to decode speakers not used in the training phase. The theme of our proposed work, increasing robustness of ASR, refers to the tendency for error rates to increase when sounds other than the desired speech corrupt the input signal or when speakers not used in training use the system. It is our view that a major factor in raising robustness concerns the inadequacies of the current spectral analysis methods. We propose to replace current analysis methods with a more appropriate technique that resists corruption by noise, and will further allow more efficient ways to adapt the ASR models to each new speaker's voice, including speech with significant accents. Our research, if successful, would lead to significantly improved ASR performance, both in increased accuracy and decreased computation. It will lead eventually to a much more agreeable way for anyone to interact with computers.

通过语音与机器进行有效的通信将促进迄今为止被键盘和电话小键盘等笨拙的界面所阻碍的交互。现在人们越来越需要与电脑互动，但这仍然主要是通过打字来完成的。即使试图通过电话查找信息，也往往需要多次听长信息，然后按下按钮，因为我们进行可靠的自动语音识别（ASR）的能力相当有限。一段时间以来，多年前建立的基本ASR方法的改进提高了性能，而基本方法没有发生根本性的变化。近年来，计算能力和内存容量的巨大增长，促使研究人员开始研究越来越困难的任务，比如通过电话连续说话、词汇量非常大、不依赖于说话者、嘈杂的讲话。对于一些有限的任务，例如识别信用卡号码，或者从中等词汇量中提取的单词，并且经常停顿，识别准确率达到99%以上，因此有了实用的商业产品。然而，在识别会话语音或嘈杂语音等更困难的任务上，进展缓慢。此外，当使用广义说话人无关模型对未在训练阶段使用的说话人进行解码时，识别错误率仍然很高。我们提出的工作主题，增加ASR的鲁棒性，指的是当期望语音以外的声音破坏输入信号或当未在训练中使用的说话者使用系统时，错误率增加的趋势。我们认为，提高鲁棒性的一个主要因素是当前光谱分析方法的不足之处。我们建议用一种更合适的技术来取代目前的分析方法，这种技术可以抵抗噪声的破坏，并将进一步允许更有效的方法来适应每个新说话者的声音，包括具有重要口音的语音。我们的研究，如果成功，将导致显著改善ASR性能，无论是在提高精度和减少计算。它最终将为任何人带来一种更令人愉快的与计算机交互的方式。